A spoofer signal is able to draw a receiver off of track of the true signal by sending a bogus signal, typically of higher power, across a target signals path in the signal space in code phase and carrier frequency. When there is a sufficiently slow divergence of the true and spoofer signals at the point where the spoofer signal and target signal cross over each other in the signal space, the signal tracker may end up following the more powerful spoofer signal. Spoofing is when the receiver is drawn off tracking of the target signal and disadvantageously tracks the spoofing signal when the receiver is designed to track only one signal in the field of view. The rate of divergence to draw the tracker off the target signal onto the spoofer signal depends upon the receiver operation. For IMU aided systems, the spoofer will have to diverge more slowly from the desired signal at the rate at which the paths cross. Typical tracking loops and ultra-tight systems may experience spoofing because tracking algorithms are designed to track only one signal within a very narrow field of view.
Traditional receivers have a very narrow field of view for tracking the desired signal. Hence they do not have information about other signals that might be close to the desired signal but not in the narrow field of view. The narrow field of view also means the tracker must have very limited uncertainty concerning the signal in order to observe the target signal. That is, existing methods track only one signal and for this reason, existing methods are not well positioned to function properly in the presence of a spoofer. Adaptive antenna antispoofing systems decrease the power level in the direction of the spoofer or maximize the power level in the direction of the desired signal. The adaptive antenna methods require multiple antenna elements. Signal processing methods rely on carrier techniques. These methods offer some improvement by reducing the bandwidth but still suffer from limitations regarding the size of the field of view and the fact that the device does not track multiple signals. The decreased size and reduced power consumption of electronics have resulted in fast processors, and small integrated circuits containing what was once an unthinkable number of GPS correlators. Integrated circuits with a large number of correlators allow the viewing of a wide field of view in the signal space but the wide field of view is only used in acquisition. Current receivers utilize tracking loops, which have a myopic field of view, and which are limited to the immediate estimated position of the track of interest.
With the increasing dependency on the Global Positioning System (GPS) for military and civilian navigation systems, it is important that the navigation receiver be able to withstand intentional signal interference with robust signal acquisition and tracking in the presence of jammers and spoofers. For this reason, inertial navigation systems have been coupled to GPS signal tracking and acquisition system for improved signal tracking and acquisition of the received signal. Inertial navigation systems (INS) have an inertial measurement unit (IMU) for processing inertial measurements. Coupling navigation data with the GPS signal tracking and acquisition system improves signal tracking and acquisition. When the inertial measurements of a navigation processor are used with a GPS signal tracking and acquisition system, the combined system is said to be tightly coupled. The tightly coupled GPS and INS method uses pseudorange and pseudorange rate measurements from the GPS receiver instead of the processed position and velocity measurements. In addition, inertial measurement unit data is usually used to assist the receiver tracking loops and enable more noise filtering than would be possible without tracking loop aiding. Ultra-tight GPS/IMU coupling further improves the performance by providing a tracking method of received Global Positioning System (GPS) signals using a correlation process that is driven by the best estimate of the navigation state vector based on inertial measurement unit samples from an inertial navigation system (INS) and GPS ephemeris data from a GPS satellite to generate replica signals for correlation with the received signals for determining pseudorange and pseudorange rate residual errors that in turn are used to update the navigation state vector for the next major cycle that generates the replica signal for the next major cycle.
All of these approaches have improved tracking and acquisition in the presence of jamming and spoofing by narrowing the received bandwidth and thereby reducing the effect of the interfering signal. The existing methods do not address the problems that occur when the spoofing signals are within the received bandwidth, instead a narrowing of the bandwidth is performed so that the spoofing signals will either not be within the signal space at all, or if inside the signal space, the spoofing signals will not remain within the signal space long enough to disturb the navigation solution. Existing receivers assume one signal is present and interpret the result of the correlation process accordingly. When in a tracking mode, the receivers examine a small portion of the signal space, which is half the width of the signal of interest and do not detect any signal outside of that region. A single correlation is based on a hypothesized value of the code and carrier, typically a receiver performs correlations on three code hypotheses the early, prompt, and late code hypotheses which typically span the width of a code chip, and one inphase and quadrature carrier hypothesis.
The pseudorange measurements are derived from an instantaneous sampling of the state of the code generator at a desired measurement epoch time. The pseudorange rate measurement is obtained by strobing the carrier loop twice over a small period of time and is a measure of a discrete change in pseudorange over a discrete period of time, determined from the carrier phase change. In the limit as the time interval goes to zero, the ratio of the delta pseudorange divided by delta time approaches the instantaneous time rate of change of the pseudorange, which is the pseudorange rate.
The tracking loops that track the incoming satellite received signal must adjust the phase and the frequency of the generated replica signal for many changing variables, such as user and satellite relative motion and user clock drifts. These tracking loops are traditionally called the code loop and the carrier loop, because the code loop tracks the phase of the Pseudorandom Noise (PRN) code and the carrier loop tracks the signal carrier frequency and the carrier phase. Although a phase-locked loop is the most common way to track a carrier signal, the GPS signals have a fifty-hertz navigation data message superimposed on the code generation process, which can potentially change the phase of the signal by 180 degrees every twenty milliseconds. To avoid a loss of lock when this occurs, a Costas tracking loop is used in place of the conventional phase lock loop. This allows the carrier to be tracked across 50 Hz data bit changes with no loss of lock. Frequency lock loops are also used for the carrier tracking and sometimes are used in combination with phase lock loops to improve robustness.
The design of tracking loop transfer functions is compromised to meet two conflicting requirements. On one hand, it is desired to have a low bandwidth of the tracking loop to filter out as much noise as possible. On the other hand, if the tracking loop is too sluggish because of a low operating bandwidth, the tracking loop cannot track the dynamics of the relative motion often caused by vehicular acceleration. Inertial aiding applies to the use of data from an IMU to assist the tracking loops or the extrapolation of the user's position and velocity. When inertial aiding is used in the context of tracking loops, the relative motion between the satellite's antenna and the user's antenna can be predicted to a certain accuracy based on the IMU measurements and the position and velocity of a satellite evaluated from the ephemeris data. A reduction of the bandwidth of the tracking loop is possible with inertial aiding because the loop need only track the errors in the aiding information as opposed to the absolute motion. A narrower bandwidth has the advantage of filtering out more noise from the loop during tracking. Tracking loops have a small field of view. Inherent in the design of tracking loops is the tracking of only one signal.
GPS sets with IMU aiding, including those with communication receivers and navigation processors, must use a large enough tracking loop bandwidth to accommodate the dynamics of the relative vehicular motion and tolerate the associated noise filtering of the tracking loop. The IMU aided GPS sets are able to use narrower bandwidth tracking loops, which decreases broadband noise power and reduces the probability that a disturbance signal will survive the noise filtering. The IMU aided GPS sets use known user's relative motion through measurements from the IMU and the satellite position and velocity data evaluated from the ephemerides to compute the line-of-sight rate between the user's antenna and the GPS satellite for providing rate data to aid the tracking loop. This allows the tracking loop gains to be reduced to a lower bandwidth so that the tracking loop only tracks the errors relative to the nominal motion provided by the IMU, user clock, and ephemeris information. The signals corresponding to the errors in the clock and ephemeris are much lower in bandwidth than the signals corresponding to the position, clock, and ephemeris. When a tracking loop operation is aided by IMU measurement, the tracking loop is then to be tightly coupled with the IMU.
Even with IMU tight coupling, and with the lower tracking loop bandwidth, the tracking loop must maintain a lock on the signal by driving the internally generated replica signal to correlate with the received satellite signal. In a tightly coupled tracking loop, the aiding IMU information is used as reference motion, but the tracking loops must still use code and carrier phase error signals in a feedback loop to drive the internally generated replica signal to match the incoming satellite received signal. If the tracking loop fails to drive the error signal to zero, the amount of error that remains uncorrected is a measurement error that is fed into the navigation-processing algorithm as an unmodeled pseudorange and pseudorange rate measurement error. This time-correlated unmodeled pseudorange and pseudorange rate measurement error limits the allowable update rate of the navigation Kalman filter, because the assumption made in the Kalman filter processing is that the measurement errors are uncorrelated in time. This update rate limitation has traditionally limited the allowable processing rate of GPS measurements from an aided tracking loop to about one hertz. Although tracking loops use all of the data over the one-second interval to maintain lock, only the recent data prior to the measurement time is actually used to generate the pseudorange and pseudorange rate measurements. Most of the available inphase (I) and quadrature (Q) correlation output measurement data over the one-second interval is essentially used to keep the tracking loop in lock. But, all of this I and Q information is not necessarily used to improve the pseudorange and pseudorange rate measurement accuracy due to the fading memory of the tracking loop having a lower but limited bandwidth. Aided tracking loops have a narrow bandwidth but still suffer from an inability to track multiple signals and the narrow field of view.
When the tracking loop error builds beyond a certain threshold value, the loop is deemed to be out of lock and pseudorange and pseudorange rate measurements are not made until the loop reacquires lock. This out of lock condition is due to the nonlinearity of the correlation process and the use of linear loop designs that are only valid for small error conditions. If the loop loses lock, the received signal must then be reacquired. The reacquisition search process requires higher received signal strength than the tracking process, so when there is a marginal signal-to-noise situation for tracking, the loop may never reacquire lock.
A GPS receiver has multiple channels, one for each satellite tracked. Each GPS channel has a respective code tracking loop and carrier tracking loop that operates independently of each other. Hence, one tracking loop may lose lock due to a low signal-to-noise ratio even though other GPS channels are being normally tracked. The information from the other good channels is not used in the inoperative channel that may be out of lock due to a marginal signal to noise ratio that is insufficient to maintain lock.
The traditional tracking loop approach determines the code phase measurement independently of the carrier phase measurement and determines the pseudorange and pseudorange rate measurements independently on each of the two L1 and L2 GPS frequencies. Each of these four measurements is independently computed. Yet these measurements are not independent because these measurements are received by only one antenna. These measurements have not been processed dependently to provide enhanced measurement accuracy and robustness that can more readily overcome interference when used collectively.
Advanced tracking loop designs have been proposed to alleviate some of the loss of lock and reacquisition problems by tightly coupling IMU data with the carrier and code tracking loops. Prior tightly coupled systems are limited by the use of Costas carrier loops for determining twenty-millisecond bit boundary data without required a priori knowledge of the data bits. Conventional tracking loops require this data bit boundary knowledge in real time to support the correlation process. Some systems have been proposed for operating a Kalman filter at a much higher rate so that the Kalman filter is considered a part of the tracking loops. These approaches suffer from the requirement for huge processor throughput because the Kalman filter, that may have twenty or more state variables, must operate at several tens of Hertz.
With unlimited integration Kalman filter processor throughput, the integration Kalman filter could be operated at extremely high rates with no loss of optimality due to time correlated tracking loop errors. This high rate operation is similar to what has been called a vector delay lock loop. A vector delay lock loop is a method to track all satellites in view simultaneously with one common algorithm. The vector delay lock loop broadens the normal aided and unaided tracking loop design approach to both code and carrier tracking on all in-view satellites. The entire algorithm must run at very high processing rates because there is no provision for federated processing. The advanced tracking loops, similar to vector loops, still have a narrow field of view for tracking only one signal and do not allow for the concurrent tracking of multiple signals.
Ultratight GPS IMU Coupling (UTC) overcomes the current processor throughput limitations, as current processors cannot support Kalman filter rates of several tens of Hertz with the large state vectors required for GPS inertial navigation. UTC decomposes the large Kalman filters into one or more federated Kalman filters within a Kalman filter processing architecture. For example, a large Kalman filter can be decomposed into two partitions including a large integration Kalman filter and high-rate prefilters that are more compatible with modern processing speed requirements. The fundamental principle is to decompose the complete formulation into suitable partitions such that the important bandwidths and models are appropriate for each partition. UTC also has the advantages that the pseudorange acceleration is estimated and the ionospheric effects are more accurately estimated using both code and carrier information. In addition, the UTC prefilters can provide goodness of fit information to the central filter so that parameter estimates are properly weighted. UTC prefilters employ discriminator and sequential filters for tracking only one signal in the narrow field of view. Multitarget tracking algorithms have been very well developed for various signal and image processing applications such as missile tracking. Typically multitarget tracking algorithms break up the process into detection, track initiation, track propagation, gating tracks, updating track hypotheses, trimming track hypotheses, and track termination. This type of processing has not been applied to GPS processing. Multitarget tracking algorithms have not been applied to GPS.
In traditional receivers, the integrate-and-dump process is limited to twenty-milliseconds due to the potential change of a data bit in the navigation message. A technique called data stripping is used to integrate for longer periods of time by using a priori knowledge of the data bit values. A large percentage of the future data bit values is predictable once it is collected, especially with prior knowledge of the data to be uploaded to the satellites. Although data stripping has been demonstrated to work, the results have been somewhat disappointing relative to predicted improvements and the logistics of determining the future navigation data message is cumbersome. Massive correlator chips are used to decrease acquisition time by checking many code phase hypotheses at one time. Once acquisition has been accomplished the receiver reverts to using only the early, prompt, and late code hypotheses and one carrier phase hypothesis, resulting in a narrow field of view.
Detection involves finding signals in the signal space. The signal space could be searched by directly examining the outputs of sensor elements that span the entire space at any given moment such a sensor system is often called a starring sensor. The signal space could be searched by fewer sensor elements that span the space over a period of time. This is referred to as the scanning sensor method. Typically the raw data from the sensors are filtered so as to separate signals from background noise. Detections are then characterized by power and location in the signal space. They are then assigned to one or more potential tracks or become part of a new track.
An effective disturbance signal must have some regularity in motion through the signal space. Gating algorithms employ signal motion models to compute an expected detection region for a track referred to as a gate. Detections that are far outside of the gate of a track will not likely be associated with that track. Detections far from any gate will cause new tracks to be initiated. Because it is possible that gating regions for various tracks overlap, detections can be tentatively assigned to more than one track hypothesis. All track hypotheses are propagated until such a time as the tracks become resolved. A track is resolved when the data points very strongly in favor of that track hypothesis over all others. Tracks are terminated after a few measurement cycles without a detection falling within proximity of its gate. When two signals cross it is possible for them to interfere with each other and give unusual measurements that do not fit either track. Multitarget tracking algorithms can anticipate and detect crossing signals, and sometimes will wait until the signals separate from each other before attempting to assign detections to tracks.
Previous GPS tracking methods have narrow fields of view that make it easy for the desired signal to drift outside of the field of view by tracking only one signal. The crossing of spoofing signals cannot be predicted and no context is available for the resolution of various tracks. The greater power of the spoofing signal aids in spoofing in traditional systems. The multiple dimensional nature of the signals are not utilized for tracking multiple signals of different types but only one signal of a given type. In addition, most existing methods disadvantageously use a narrow field of view when tracking in the field of view. These and other disadvantages are eliminated or reduced using the invention.